Unsupervised Domain Adaptation for Segmentation with Black-box Source Model
This addresses privacy concerns in cross-domain collaborations for medical imaging segmentation, but it is incremental as it builds on existing UDA methods.
The paper tackled the problem of unsupervised domain adaptation for segmentation when source data and model parameters are private, proposing a method using a black-box source model and achieving performance comparable to white-box approaches on the BraTS 2018 database.
Unsupervised domain adaptation (UDA) has been widely used to transfer knowledge from a labeled source domain to an unlabeled target domain to counter the difficulty of labeling in a new domain. The training of conventional solutions usually relies on the existence of both source and target domain data. However, privacy of the large-scale and well-labeled data in the source domain and trained model parameters can become the major concern of cross center/domain collaborations. In this work, to address this, we propose a practical solution to UDA for segmentation with a black-box segmentation model trained in the source domain only, rather than original source data or a white-box source model. Specifically, we resort to a knowledge distillation scheme with exponential mixup decay (EMD) to gradually learn target-specific representations. In addition, unsupervised entropy minimization is further applied to regularization of the target domain confidence. We evaluated our framework on the BraTS 2018 database, achieving performance on par with white-box source model adaptation approaches.